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Synthetic data pipeline generates annotated training data for scratch detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ScratchSim, a procedural synthetic data pipeline using BlenderProc to generate annotated training data for surface scratch detection. This method addresses the challenge of limited annotated defect data in industrial quality control. The pipeline offers configurable material appearance, camera modes, and domain randomization, producing automatic COCO-format annotations. Evaluations demonstrated that fine-tuning models with synthetic data outperforms real-only training, and mixed training effectively recovers performance with scarce real data, showing promise for on-device industrial inspection. AI

IMPACT Enables scalable defect detection in industrial settings by reducing reliance on large, real-world annotated datasets.

RANK_REASON The cluster describes a research paper detailing a new synthetic data generation pipeline for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Synthetic data pipeline generates annotated training data for scratch detection

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The cluster describes a research paper detailing a new synthetic data generation pipeline for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Paul Julius K\"uhn, Saptarshi Neil Sinha, Tiago Kleist, Richard Hoffmann, Arjan kuijper, Michael Weinmann ·

    ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

    arXiv:2607.27065v1 Announce Type: new Abstract: While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging. This paper presents a procedural ren…

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